遍历数据框字典 [英] Loop through a dictionary of dataframes

问题描述

我有一组数据框，这些数据框代表我放入字典中的需求情景.我需要遍历字典中的每个数据框以重新索引和重新采样等，然后返回字典.当我遍历数据帧列表时，下面的代码可以完美地工作，但是我需要维护每个场景的身份，因此需要字典.

I have a set of dataframes that represent scenarios of demand that I have put into a dictionary. I need to loop through each dataframe in the dictionary to reindex and resample etc. and the return to the dictionary. The below code works perfectly when I loop through a list of dataframes but I need to maintain the identity of each scenario, hence the dictionary.

这是与数据帧列表配合使用的代码:

This is the code that works with a list of dataframes:

demand_dfs_list = [low_demand_df, med_low_demand_df, bc_demand_df, med_high_demand_df, high_demand_df]
dates = pd.date_range(start='2020-10-01', end='2070-09-30', freq='D')

demand_dfs_datetime = []
for df in demand_dfs_list:
    df.index = pd.to_datetime(df.index, format='%Y')
    df = df.tshift(-92, 'D')
    df = df.resample('D').ffill()
    df = df.reindex(dates)
    demand_dfs_datetime.append(df)

这是我尝试的字典形式:

This is what I have tried in dictionary form:

demand_scenarios = {'low': low_demand_df, 'medium_low': med_low_demand_df, 'medium': bc_demand_df, 'medium_high': med_high_demand_df, 'high': high_demand_df}
dates = pd.date_range(start='2020-10-01', end='2070-09-30', freq='D')

demand_dict = {}
    for df in demand_scenarios:
        [df].index = pd.to_datetime([df].index, format='%Y')
        [df] = [df].tshift(-92, 'D')
        [df] = [df].resample('D').ffill()
        [df] = [df].reindex(dates)
        demand_dict[df] = df

关注问题 我使用以下命令将上面的demand_dict字典传递到xarray中:

FOLLOW UP QUESTION I passed the above demand_dict dictionary into an xarray using the below:

demand_xarray = xr.Dataset(demand_dict, coords = {'customers': customers, 'time': dates})

但是我的数据集如下所示:

However my dataset looks like the following:

<xarray.Dataset>
Dimensions:      (customers: 28, dim_0: 18262, dim_1: 28, time: 18262)
Coordinates:
  * dim_0        (dim_0) datetime64[ns] 2020-10-01 2020-10-02 ... 2070-09-30
  * dim_1        (dim_1) object 'Customer_1' ... 'Customer_x'
  * customers    (customers) <U29 'Customer_1' ... 'Customer_x'
  * time         (time) datetime64[ns] 2020-10-01 2020-10-02 ... 2070-09-30
Data variables:
    low          (dim_0, dim_1) float64 0.52 0.528 3.704 ... 7.744 0.92 64.47
    medium_low   (dim_0, dim_1) float64 0.585 0.594 4.167 ... 8.712 1.035 72.53
    medium       (dim_0, dim_1) float64 0.65 0.66 4.63 12.6 ... 9.68 1.15 80.59
    medium_high  (dim_0, dim_1) float64 0.715 0.726 5.093 ... 10.65 1.265 88.65
    high         (dim_0, dim_1) float64 0.78 0.792 5.556 ... 11.62 1.38 96.71

当我尝试像这样使用drop_dims函数时:

When I try and use the drop_dims function like so:

demand_xarray = xr.Dataset(demand_dict, coords = {'customers': customers, 'time': dates}).drop_dims(dim_0, dim_1)

我得到了错误:

AttributeError: 'Dataset' object has no attribute 'drop_dims'

知道为什么我会收到此错误吗?

Any idea why I am getting this error?

推荐答案

demand_scenarios = {'low': low_demand_df, 'medium_low': med_low_demand_df, 'medium': bc_demand_df, 'medium_high': med_high_demand_df, 'high': high_demand_df}
dates = pd.date_range(start='2020-10-01', end='2070-09-30', freq='D')

demand_dict = {}
    for key, df in demand_scenarios.items():
        df.index = pd.to_datetime([df].index, format='%Y')
        df = df.tshift(-92, 'D')
        df = df.resample('D').ffill()
        df = df.reindex(dates)
        demand_dict[key] = df

items()返回字典的键和值

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